{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fdadce35",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yzd\\anaconda3\\lib\\site-packages\\scipy\\__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.25.0 is required for this version of SciPy (detected version 1.25.0\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "# @Time : 2022/10/2 15:15\n",
    "# @Author : 中意灬\n",
    "# @FileName: 基于LSTM的股票预测.py\n",
    "# @Software: PyCharm\n",
    "\"\"\"第一步：导入相关的库\n",
    "https://blog.csdn.net/qq_55977554/article/details/127143319\n",
    "\"\"\"\n",
    "import math\n",
    "import os.path\n",
    "import tensorflow as tf\n",
    "import tushare as ts\n",
    "import numpy as np\n",
    "import tensorflow\n",
    "import pandas as pd\n",
    "from tensorflow.keras.layers import Dense,LSTM,Dropout\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras import Model\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error,mean_absolute_error\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ff082bf4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==========loading data==========\n"
     ]
    }
   ],
   "source": [
    "\"\"\"第二步：准备数据\"\"\"\n",
    " \n",
    "datapath='data.csv'\n",
    "if os.path.exists(datapath):\n",
    "    print(\"==========loading data==========\")\n",
    "    data=pd.read_csv(datapath)\n",
    "    training_set=data.iloc[0:2427-300,2:3]\n",
    "    test_set=data.iloc[2427-300:,2:3]\n",
    "    test_yz=data.iloc[2427-300:,2:3]\n",
    "else:\n",
    "    ts.set_token('f9e62b42d9f31fbf0267d9ba52204d37c5fef60f3d6091e9820c40a1')  # 这儿的token需要自己去turshare注册申请\n",
    "    df = ts.get_k_data('601012', ktype='D', start='2012-01-01', end='2023-06-30')\n",
    "    df.to_csv(datapath)\n",
    "    data = pd.read_csv('data.csv')\n",
    "    training_set = data.iloc[0:2427 - 300, 2:3].values\n",
    "    test_set = data.iloc[2427 - 300:, 2:3].values\n",
    "#归一化\n",
    "sc=MinMaxScaler(feature_range=(0,1))#初始化对象定义归一化：归一化到（0-1）间\n",
    "training_set_scaler=sc.fit_transform(training_set)#求得训练集的最大值，最小值这些训练集固有的属性（反归一化所需要这些属性），并在训练集上进行归一化\n",
    "test_set=sc.transform(test_set)#利用训练集的属性对测试集进行归一化\n",
    "# print(training_set_scaler)\n",
    "x_train=[]\n",
    "x_test=[]\n",
    "y_train=[]\n",
    "y_test=[]\n",
    "#利用for循环，遍历整个训练集，将训练集连续60天的数据作为训练特征x_train,第61天数据作为训练标签y_train\n",
    "for i in range(60,len(training_set_scaler)):\n",
    "    x_train.append(training_set_scaler[i-60:i,0])\n",
    "    y_train.append(training_set_scaler[i,0])\n",
    "#将训练特征和标签转换神经网络的输入格式，使x_train符合LSTM输入要求：[送入样本数,循环核时间展开步骤,每个时间步输入特征个数]\n",
    "x_train,y_train=np.array(x_train),np.array(y_train)\n",
    "x_train=np.reshape(x_train,(len(x_train),60,1))\n",
    "#利用for循环，遍历整个训练集，将训练集连续60天的数据作为测试特征x_test,第61天数据作为测试标签y_test\n",
    "for i in range(60,len(test_set)):\n",
    "    x_test.append(test_set[i-60:i,0])\n",
    "    y_test.append(test_set[i,0])\n",
    "#将测试特征和标签转换神经网络的输入格式，使x_train符合LSTM输入要求：[送入样本数,循环核时间展开步骤,每个时间步输入特征个数]\n",
    "x_test,y_test=np.array(x_test),np.array(y_test)\n",
    "x_test=np.reshape(x_test,(len(x_test),60,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8f32f85c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"第三步：使用class类搭建LSTM神经网络模型\"\"\"\n",
    "class LSTMModel(Model):\n",
    "    def __init__(self):\n",
    "        super(LSTMModel, self).__init__()\n",
    "        self.l1=LSTM(256,activation='tanh',return_sequences=True)\n",
    "        self.d1=Dropout(0.2)\n",
    "        self.l2=LSTM(128,activation='tanh',return_sequences=False)\n",
    "        self.d2=Dropout(0.2)\n",
    "        self.f1=Dense(1)\n",
    "    def call(self,x):\n",
    "        x=self.l1(x)\n",
    "        x=self.d1(x)\n",
    "        x=self.l2(x)\n",
    "        x=self.d2(x)\n",
    "        x=self.f1(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5733710c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==========load the model==========\n"
     ]
    }
   ],
   "source": [
    "\"\"\"第四步：使用model.compile配置神经网络参数\"\"\"\n",
    "model=LSTMModel()\n",
    "model.compile(optimizer=tf.keras.optimizers.Adam(0.001),#自己设定adam的学习率，尽量先设置小，大了会收敛过快\n",
    "              loss=\"mean_squared_error\")#不必观察metrics值，没必要，只用观察loss值就可以\n",
    "\n",
    "checkpoint_save_path=\"LSTN.ckpt\"\n",
    "if os.path.exists(checkpoint_save_path+'.index'):\n",
    "    print(\"==========load the model==========\")\n",
    "    model.load_weights(checkpoint_save_path)\n",
    "cp_callback=tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path,\n",
    "                                               save_weights_only=True,\n",
    "                                               save_best_only=True,\n",
    "                                               moniter='val_loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4a716181",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "65/65 [==============================] - 11s 171ms/step - loss: 2.3818e-04 - val_loss: 6.9007e-04\n",
      "Epoch 2/20\n",
      "65/65 [==============================] - 11s 170ms/step - loss: 2.4887e-04 - val_loss: 7.6730e-04\n",
      "Epoch 3/20\n",
      "65/65 [==============================] - 11s 166ms/step - loss: 2.5066e-04 - val_loss: 0.0011\n",
      "Epoch 4/20\n",
      "65/65 [==============================] - 11s 170ms/step - loss: 2.3578e-04 - val_loss: 0.0012\n",
      "Epoch 5/20\n",
      "65/65 [==============================] - 11s 167ms/step - loss: 2.8426e-04 - val_loss: 0.0043\n",
      "Epoch 6/20\n",
      "65/65 [==============================] - 11s 169ms/step - loss: 3.1002e-04 - val_loss: 0.0035\n",
      "Epoch 7/20\n",
      "65/65 [==============================] - 11s 170ms/step - loss: 2.3970e-04 - val_loss: 0.0038\n",
      "Epoch 8/20\n",
      "65/65 [==============================] - 11s 173ms/step - loss: 2.1248e-04 - val_loss: 8.2442e-04\n",
      "Epoch 9/20\n",
      "65/65 [==============================] - 11s 174ms/step - loss: 1.7189e-04 - val_loss: 6.8597e-04\n",
      "Epoch 10/20\n",
      "65/65 [==============================] - 11s 173ms/step - loss: 2.3376e-04 - val_loss: 0.0046\n",
      "Epoch 11/20\n",
      "65/65 [==============================] - 11s 167ms/step - loss: 1.9590e-04 - val_loss: 0.0015\n",
      "Epoch 12/20\n",
      "65/65 [==============================] - 11s 170ms/step - loss: 2.5667e-04 - val_loss: 0.0021\n",
      "Epoch 13/20\n",
      "65/65 [==============================] - 11s 169ms/step - loss: 2.7342e-04 - val_loss: 6.6123e-04\n",
      "Epoch 14/20\n",
      "65/65 [==============================] - 11s 174ms/step - loss: 2.4055e-04 - val_loss: 8.5734e-04\n",
      "Epoch 15/20\n",
      "65/65 [==============================] - 12s 179ms/step - loss: 2.0971e-04 - val_loss: 0.0013\n",
      "Epoch 16/20\n",
      "65/65 [==============================] - 12s 179ms/step - loss: 2.2385e-04 - val_loss: 0.0017\n",
      "Epoch 17/20\n",
      "65/65 [==============================] - 11s 174ms/step - loss: 1.9979e-04 - val_loss: 0.0014\n",
      "Epoch 18/20\n",
      "65/65 [==============================] - 11s 167ms/step - loss: 3.0877e-04 - val_loss: 0.0039\n",
      "Epoch 19/20\n",
      "65/65 [==============================] - 11s 170ms/step - loss: 2.4530e-04 - val_loss: 0.0013\n",
      "Epoch 20/20\n",
      "65/65 [==============================] - 11s 170ms/step - loss: 2.4223e-04 - val_loss: 6.9889e-04\n",
      "Model: \"lstm_model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 multiple                  264192    \n",
      "                                                                 \n",
      " dropout (Dropout)           multiple                  0         \n",
      "                                                                 \n",
      " lstm_1 (LSTM)               multiple                  197120    \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         multiple                  0         \n",
      "                                                                 \n",
      " dense (Dense)               multiple                  129       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 461,441\n",
      "Trainable params: 461,441\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"第五步：用model.fit训练神经网络模型\"\"\"\n",
    "history=model.fit(x_train,y_train,batch_size=32,epochs=20,validation_data=(x_test,y_test),validation_freq=1,callbacks=[cp_callback])\n",
    "#参数提取\n",
    "file=open('weights.txt','w')\n",
    "for v in model.trainable_variables:\n",
    "    file.write(str(v.name)+'\\n')\n",
    "    file.write(str(v.shape)+'\\n')\n",
    "    file.write(str(v.numpy())+'\\n')\n",
    "\"\"\"第六步：使用model.summary打印神经网络结构\"\"\"\n",
    "model.summary()\n",
    " \n",
    "#绘制loss图像\n",
    "plt.figure()\n",
    "plt.plot(history.history['loss'],label='loss')\n",
    "plt.plot(history.history['val_loss'],label='val_loss')\n",
    "plt.title('Train and Validation loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4d09f23b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16/16 [==============================] - 1s 48ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#模型预测\n",
    "predict_stock_openprice=model.predict(x_test)\n",
    "#对预测数据反归一化\n",
    "predict_stock_openprice=sc.inverse_transform(predict_stock_openprice)\n",
    "#对真实数据反归一化\n",
    "real_stock_openprice=sc.inverse_transform(test_set[60:])\n",
    "#可视化\n",
    "plt.figure()\n",
    "plt.plot(real_stock_openprice,color='r',label='real')\n",
    "plt.plot(predict_stock_openprice,color='b',label='predict')\n",
    "plt.legend()\n",
    "plt.show()\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c2b5a2c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "均方误差： 2.750248128819235\n",
      "均方误差根： 1.6583872071441081\n",
      "平局绝对误差： 1.3131313265075681\n",
      "输入你要预测后多少个数据：50\n",
      "1/1 [==============================] - 0s 23ms/step\n",
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     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##模型预测效果量化，数值越小，效果越好\n",
    "#MSE 均方误差-->E[(预测值-真实值)^2]\n",
    "mse=mean_squared_error(predict_stock_openprice,real_stock_openprice)\n",
    "#RMSE 均方误差根-->sqrt(mse)\n",
    "rmse=math.sqrt(mean_squared_error(predict_stock_openprice,real_stock_openprice))\n",
    "#MAE 平均绝对误差-->E(|预测值-真实值|)\n",
    "mae=mean_absolute_error(predict_stock_openprice,real_stock_openprice)\n",
    "print('均方误差：',mse)\n",
    "print('均方误差根：',rmse)\n",
    "print('平局绝对误差：',mae)\n",
    " \n",
    "#对未知数据预测\n",
    "preNum=int(input('输入你要预测后多少个数据：'))\n",
    "a = test_set[len(test_set) - 60:, 0]\n",
    "c=[]#存储预测后的数据\n",
    "for i in range(preNum):\n",
    "    b=np.reshape(a,(1,60,1))\n",
    "    pre=model.predict(b)\n",
    "    a=a.tolist()\n",
    "    del a[0]\n",
    "    a.extend(pre[0])\n",
    "    c.extend(pre)\n",
    "    a=np.array(a)\n",
    "test_set=np.array(test_set)\n",
    "c=sc.inverse_transform(c)\n",
    "plt.figure()\n",
    "plt.plot(sc.inverse_transform(test_set[60:]),color='b',label='real')\n",
    "x=np.arange(len(test_set[60:]),len(test_set[60:])+preNum)\n",
    "plt.plot(x,c,color='r')\n",
    "plt.plot(predict_stock_openprice,color='r',label='predict')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3cd2bd31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[34.82181807],\n",
       "       [31.37496978],\n",
       "       [30.10308921],\n",
       "       [29.05197938],\n",
       "       [27.96608003],\n",
       "       [26.91651331],\n",
       "       [25.94431238],\n",
       "       [25.05483012],\n",
       "       [24.24293055],\n",
       "       [23.50123753],\n",
       "       [22.82217625],\n",
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